Wearable Sensor System for Neurological Motor Skill Therapy
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Solution Overview
Problem
Current medical practices for patients with neurological disorders, such as ataxia, multiple sclerosis, and cerebellar degeneration, often fail to fully recover motor skills deficiencies, as physical therapy sessions do not provide comprehensive and personalized rehabilitation.
Innovation Solution
A system comprising sensors and transducers affixed to clothing, connected to a processing system and a machine learning model, which processes sensor data to provide personalized therapy by adjusting pressure points on the body to improve motor skills and mobility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical therapy sessions are used to treat neurological disorders, then motor skills can be improved to some extent, but the therapy is not comprehensive or personalized enough to fully recover motor skills deficiencies
Solution Approach 1:
The system continuously monitors patient movement data through sensors and uses machine learning models to analyze performance, providing real-time feedback that enables dynamic adjustment of therapy parameters. This closed-loop feedback mechanism allows the system to adapt treatment to individual patient needs and progress, resolving the contradiction between therapy effectiveness and personalization.
Solution Approach 2:
The therapy system transitions from static, pre-programmed physical therapy protocols to dynamic, adaptive treatment that automatically adjusts parameters based on real-time patient performance data. The machine learning model continuously learns from patient responses and modifies therapy delivery accordingly, enabling both comprehensive monitoring and personalized adaptation.
2Productivity
If traditional physical therapy is provided, then some motor skill improvement occurs, but continuous monitoring and data-driven adjustments are not achieved
Solution Approach 1:
The system implements continuous therapy delivery through automated transducer activation based on real-time sensor data, eliminating gaps between therapy sessions. Sensors continuously monitor patient movement and the machine learning model continuously processes data to adjust therapy parameters, ensuring uninterrupted, data-driven treatment that maximizes therapeutic effectiveness.
Solution Approach 2:
The system automatically monitors patient progress, analyzes performance data through machine learning algorithms, and adjusts therapy parameters without requiring constant clinician intervention. This self-adjusting capability enables continuous data collection and utilization, transforming raw sensor data into actionable therapy modifications that improve productivity while preventing information loss.
3Ease of operation
If standardized physical therapy protocols are used, then treatment consistency is maintained, but individual patient needs and progress are not adequately addressed
Solution Approach 1:
The system automatically adapts standardized therapy protocols to individual patient needs through machine learning algorithms that analyze real-time sensor data. The machine learning model self-adjusts therapy parameters based on patient performance, eliminating the need for manual customization while maintaining treatment consistency. This enables the system to simultaneously achieve ease of operation through automated protocols and adaptability through data-driven personalization.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device, including: a sensor affixed to an article of clothing; a transducer affixed to the article of clothing; a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: receiving sensor data from the sensor; processing the sensor data; sending the sensor data to a trained machine learning (ML) model having as inputs the sensor data, and providing as output, control data to control the transducer; receiving the control data from the trained ML model; and sending the control data to the transducer to provide therapy to a patient. Other embodiments are disclosed.